{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)   # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)        # type numpy.ndarray\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23601924198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "#     idxs = np.flatnonzero(y_train == y)      \n",
    "    idxs = np.where(y_train==y)[0]   # np.where返回 (array([], dtype),)，所以加[0]\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)  #replace为False表示不重复选择\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23608894128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "# 如果没有使用np.sqrt对结果开根号，则图像偏黑\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: \n",
    "\n",
    "(1) 白色行原因：某个测试样本和所有的训练样本的距离都很大，即该样本和训练集中样本的相似性较低\n",
    "\n",
    "(2) 白色列原因：某个训练样本和所有的测试样本相似性都很差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 2**\n",
    "We can also other distance metrics such as L1 distance.\n",
    "The performance of a Nearest Neighbor classifier that uses L1 distance will not change if (Select all that apply.):\n",
    "1. The data is preprocessed by subtracting the mean.\n",
    "2. The data is preprocessed by subtracting the mean and dividing by the standard deviation.\n",
    "3. The coordinate axes for the data are rotated.\n",
    "4. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "1\n",
    "\n",
    "*Your explanation*:\n",
    "\n",
    "取1原因：$d(I_1,I_2)=\\sum_p\\mid I_1^p-I_2^p\\mid$，如果只是减去均值，该公式中会抵消掉；但如果再除以标准差，则可以将该值提出了，则整体距离会降低该倍数；如果改变坐标系，那每个特征的值变化，导致各特征的差绝对值也会改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 32.314084 seconds\n",
      "One loop version took 86.023477 seconds\n",
      "No loop version took 0.381012 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**使用一个循环比使用两个循环的时间长，猜想：循环和向量化后的矩阵混搭可能会影响效率？？？**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.526000\n",
      "k = 1, accuracy = 0.514000\n",
      "k = 1, accuracy = 0.528000\n",
      "k = 1, accuracy = 0.556000\n",
      "k = 1, accuracy = 0.532000\n",
      "k = 3, accuracy = 0.478000\n",
      "k = 3, accuracy = 0.498000\n",
      "k = 3, accuracy = 0.480000\n",
      "k = 3, accuracy = 0.532000\n",
      "k = 3, accuracy = 0.508000\n",
      "k = 5, accuracy = 0.496000\n",
      "k = 5, accuracy = 0.532000\n",
      "k = 5, accuracy = 0.560000\n",
      "k = 5, accuracy = 0.584000\n",
      "k = 5, accuracy = 0.560000\n",
      "k = 8, accuracy = 0.524000\n",
      "k = 8, accuracy = 0.564000\n",
      "k = 8, accuracy = 0.546000\n",
      "k = 8, accuracy = 0.580000\n",
      "k = 8, accuracy = 0.546000\n",
      "k = 10, accuracy = 0.530000\n",
      "k = 10, accuracy = 0.592000\n",
      "k = 10, accuracy = 0.552000\n",
      "k = 10, accuracy = 0.568000\n",
      "k = 10, accuracy = 0.560000\n",
      "k = 12, accuracy = 0.520000\n",
      "k = 12, accuracy = 0.590000\n",
      "k = 12, accuracy = 0.558000\n",
      "k = 12, accuracy = 0.566000\n",
      "k = 12, accuracy = 0.560000\n",
      "k = 15, accuracy = 0.504000\n",
      "k = 15, accuracy = 0.578000\n",
      "k = 15, accuracy = 0.556000\n",
      "k = 15, accuracy = 0.564000\n",
      "k = 15, accuracy = 0.548000\n",
      "k = 20, accuracy = 0.540000\n",
      "k = 20, accuracy = 0.558000\n",
      "k = 20, accuracy = 0.558000\n",
      "k = 20, accuracy = 0.564000\n",
      "k = 20, accuracy = 0.570000\n",
      "k = 50, accuracy = 0.542000\n",
      "k = 50, accuracy = 0.576000\n",
      "k = 50, accuracy = 0.556000\n",
      "k = 50, accuracy = 0.538000\n",
      "k = 50, accuracy = 0.532000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.540000\n",
      "k = 100, accuracy = 0.526000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.526000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array_split(X_train, num_folds)   # 分割数据集为 num_folds份\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "classifier = KNearestNeighbor()           # 创建KNN实例\n",
    "for k in k_choices:                       # 选择不同的k\n",
    "    k_to_accuracies[k] = []\n",
    "    for val_idx in range(num_folds):\n",
    "        # 将验证集外的数据组合为训练集      \n",
    "        X_train_new = np.vstack([X_train_folds[train_idx] for train_idx in range(num_folds) if train_idx != val_idx])\n",
    "        y_train_new = np.hstack([y_train_folds[train_idx] for train_idx in range(num_folds) if train_idx != val_idx])\n",
    "        classifier.train(X_train_new, y_train_new)\n",
    "        \n",
    "        # 使用验证集得到准确率\n",
    "        y_val_pred = classifier.predict(X_train_folds[val_idx], k=k)\n",
    "        num_correct = np.sum(y_val_pred == y_train_folds[val_idx])\n",
    "        accuracy = float(num_correct) / num_test\n",
    "        k_to_accuracies[k].append(accuracy) \n",
    "    \n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# # Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23603808048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)   # 每个k对应num_folds个准确率\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)    # 绘制误差线\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question 3**\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The training error of a 1-NN will always be better than that of 5-NN.\n",
    "2. The test error of a 1-NN will always be better than that of a 5-NN.\n",
    "3. The decision boundary of the k-NN classifier is linear.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "*Your Answer*:\n",
    "1、 4\n",
    "\n",
    "*Your explanation*:\n",
    "\n",
    "选1：使用1-NN可以比5-NN训练数据过拟合的简单，所以相应的训练误差也很低，但泛化性能是5-NN相对较好，所以测试误差较低，K-NN分类可视化图看出决策边界不是线性的，K-NN分类器中决策边界没有线性的参数，只是比较测试和训练样本距离来判别分类\n",
    "\n",
    "选4：训练模型时只是将训练集简单的记住，在预测过程中要对比测试样本和每一个训练集的图片，所以测试的时间会随着训练集样本数的增加而增加"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
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